Contents

svg Causal graphs network Mar_15_2010

pvar < .06. Showing Language and Distance effects for the dependent variables. The sizes of nodes show the extent of spatial clustering. Not shown are the negative versus positive or nonsignificant linguistic clustering. The colors show the layers of the directed asymetric graph (DAG).

pvar < .06. Showing Language and Distance effects 2 for the dependent variables. Here the dependent variables begin at the two bottom rows, coded yellow for significant negative linguistic effects, green otherwise, and sizes of those nodes (which were dependent variables) showing relative significance of spatial clustering. This graph also minimizes the length of lines between levels. 10 dependent variables are at the lowest levels, 2 at the next level up, 1 at the third level, and only 3 independent variables at the top level. All features of the analysis are then shown in the causal graphs, except the regression coefficients as labels for the lines.

Each downward line in the graphs has a signed regression coefficient[1] from two-stage OLS using the software of Eff, E. Anthon, and Malcolm Dow. 2009. How to Deal with Missing Data and Galton's Problem in Cross-Cultural Survey Research : A Primer for R, in Structure and Dynamics: eJournal of Anthropological and Related Sciences 3#3 art 1. Spatial and linguistic clustering effects are estimated in stage one and used to make a new estimated Galton-effects variable that is included in stage two along with other independent variables for which the causal effects regression coefficients are estimated. The statistical diagnostics test whether the error terms are now free of language and distance autocorrelation, heteroscedasticity (bunching of error terms), variables with significant effects that are not specified in the final model but which reside in the set of initial variables, or whether there are variables among those specified that would be more predictive when logged.

↑The unstandardized regression coefficients for each of the arrows are shown only for the triangular graph #6 to show an example of calculation of both direct and indirect effects according to the causal graphs methods of Judea Pearl. The magnitude of causal effects of the independent variables for each dependent variable can eventually be calculated when the fuller structure of these graphs, taking each independent variable as a dependent one, is estimated.

The independent variables with the most dependent variables effects are these:

svg Causal graphs network Mar_9_2010 (updated)

Viewable only in Mozilla firefox, these four causal graphs have black solid lines for positive regression coefficients between independent vars and dependent vars (downward lines), and red dotted lines for negative coefficients.

The thickness of the lines reflects significance, starting at pvar <.06 (thin lines) up to pvar <.0003 (thick lines).

3. pvar < .06 - you can click the square boxes for URLs that provide background information. The four levels, which form a directed acyclic graph (DAG), represent, first, variables that appear only as predictors, then ones that are both dependent (from above) and independent (to the level below) in successive levels. Colors and sizes of nodes are those of graphs 1 and 2 above.

5. pvar < .06 bicomponent layers with one transitive subgraph - can you see it?

6. pvar < .06 showing how to compute causal effects that are both direct and indirect. This example shows how Causal relations are not functionally consistent, as in a graph where signs are balanced (positive products of signs in a closed circuit).

My perception is that everything in graphs 1-5 has a structure consistent with a DAG structure that is computable for causal graphs (for each dependent variable) according to Pearl. Is this correct? The regression coefficients are computed in the second stage of the 2SLS, with the endogenous variables (spatial and language clustering coefficients) estimated in the first stage for use as predictors in the second stage.

svg network 3_3_10 all obsolete

clickable svg of new network of findings by project Working69Copy2 seems like the left wing of results deal with sex, rape, interpersonal violence, police, control of dvellings have one them then the right wing deals with frat-int-groups, stress on resource, war/fighting, wealth/poor differences, (low fe-)male agriculture, and individual freedom to choose a spouse. These intersect in the middle variable, money. What is needed now is to put red links for negative relations, and thicker links for greater significance. Because there are so many links, its probably necessary in order to estimate causal effects to eliminate all but the most significan variables, e.g., pvar < .01.

v453 EM-18iv corpun - you specified 153 by mistake. Thus, is not on the mapping site.

BIG CORRECTION Doug 13:53, 30 November 2009 (PST) to set the record straight: When you switched to depvarname<="wifebeating", Hiu Kwan. which is v453 you asked for v153 by mistake. To set the record straight since v153 was TECHNOLOGICAL SPECIALIZATION I made two changes: one to let polispec be v153 and then to let wifebeating be v453. Then I reran your results.